Enhanced neonatal screening for sickle cell disease: Human-guided deep learning with CNN on isoelectric focusing images

Kpangni Alex Jérémie Koua, Cheikh Talibouya Diop, Lamine Diop, Mamadou Diop

Article ID: 6121
Vol 8, Issue 9, 2024

VIEWS - 127 (Abstract) 34 (PDF)

Abstract


Accurate detection of abnormal hemoglobin variations is paramount for early diagnosis of sickle cell disease (SCD) in newborns. Traditional methods using isoelectric focusing (IEF) with agarose gels are technician-dependent and face limitations like inconsistent image quality and interpretation challenges. This study proposes a groundbreaking solution using deep learning (DL) and artificial intelligence (AI) while ensuring human guidance throughout the process. The system analyzes IEF gel images with convolutional neural networks (CNNs), achieving over 98% accuracy in identifying various SCD profiles, far surpassing the limitations of traditional methods. Furthermore, the system addresses ambiguities by incorporating an “Unconfirmed” category for unclear cases and assigns probability values to each classification, empowering clinicians with crucial information for informed decisions. This AI-powered tool, named SCScreen, seamlessly integrates machine learning with medical expertise, offering a robust, efficient, and accurate solution for SCD screening. Notably, SCScreen tackles the previously challenging diagnosis of major sickle cell syndromes (SDM) in newborns. This research has the potential to revolutionize SCD management. By strengthening screening platforms and potentially reducing costs, SCScreen paves the way for improved healthcare outcomes for newborns with SCD, potentially saving lives and improving the quality of life for affected individuals.

Keywords


SCD; IEF; agarose gel; CNN; visualization; healthcare data analysis

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References


Adekile, A. (2020). The Genetic and Clinical Significance of Fetal Hemoglobin Expression in Sickle Cell Disease. Medical Principles and Practice, 30(3), 201–211. https://doi.org/10.1159/000511342

Adeniyi, A. E., Ayoola, J. B., Farhaoui, Y., et al. (2024). Comparative Study for Predicting Melanoma Skin Cancer Using Linear Discriminant Analysis (LDA) and Classification Algorithms. In: Farhaoui, A. Y., Hussain, T., Saba, H., et al. (editors). Artificial Intelligence, Data Science and Applications. Springer Nature Switzerland. pp. 326–338. https://doi.org/10.1007/978-3-031-48465-0_42

Alagu, S., Ganesan, K., & K., B. B. (2022). A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images. Biomedical Engineering, Biomedizinische Technik, 68(2), 175–185. https://doi.org/10.1515/bmt-2021-0127

Aliyu, H. A., Razak, M. A. A., & Sudirman, R. (2019). Segmentation and detection of sickle cell red blood image. AIP Conference Proceedings. https://doi.org/10.1063/1.5133919

Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., et al. (2020). Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis. Electronics, 9(3), 427. https://doi.org/10.3390/electronics9030427

Arishi, W. A., Alhadrami, H. A., & Zourob, M. (2021). Techniques for the Detection of Sickle Cell Disease: A Review. Micromachines, 12(5), 519. https://doi.org/10.3390/mi12050519

Bäuerle, A., Cabrera, Á. A., Hohman, F., et al. (2022). Symphony: Composing Interactive Interfaces for Machine Learning. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3502102

Chen, C. X., Funkenbusch, G. T., & Wax, A. (2023). Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning. International Journal of Molecular Sciences, 24(15), 11885. https://doi.org/10.3390/ijms241511885

Daniel, Y., Elion, J., Allaf, B., et al. (2019). Newborn Screening for Sickle Cell Disease in Europe. International Journal of Neonatal Screening, 5(1), 15. https://doi.org/10.3390/ijns5010015

Das, P. K., Meher, S., Panda, R., et al. (2020). A Review of Automated Methods for the Detection of Sickle Cell Disease. IEEE Reviews in Biomedical Engineering, 13, 309–324. https://doi.org/10.1109/rbme.2019.2917780

De Haan, K., Ceylan Koydemir, H., Rivenson, Y., et al. (2020). Automated screening of sickle cells using a smartphone-based microscope and deep learning. Npj Digital Medicine, 3(1). 76. https://doi.org/10.1038/s41746-020-0282-y

Elendu, C., Amaechi, D. C., Alakwe-Ojimba, C. E., et al. (2023). Understanding Sickle cell disease: Causes, symptoms, and treatment options. Medicine, 102(38), e35237. https://doi.org/10.1097/md.0000000000035237

El-Haj, N., & Hoppe, C. C. (2018). Newborn Screening for SCD in the USA and Canada. International Journal of Neonatal Screening, 4(4), 36. https://doi.org/10.3390/ijns4040036

Frömmel, C. (2018). Newborn Screening for Sickle Cell Disease and Other Hemoglobinopathies: A Short Review on Classical Laboratory Methods—Isoelectric Focusing, HPLC, and Capillary Electrophoresis. International Journal of Neonatal Screening, 4(4), 39. https://doi.org/10.3390/ijns4040039

Geng, Z., Xu, Y., Wang, B. N., et al. (2023). Target Recognition in SAR Images by Deep Learning with Training Data Augmentation. Sensors, 23(2), 941. https://doi.org/10.3390/s23020941

Goceri, E. (2023). Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review, 56(11), 12561–12605. https://doi.org/10.1007/s10462-023-10453-z

Gueye, B., Tacko Diop, C., Marième Diagne, N., et al. (2020). Study of Prognostic Factors of Death in Children with Sickle Cell Diseases Followed at the Albert Royer National Children’ s Hospital Center, Dakar, Senegal. American Journal of Pediatrics, 6(1), 1. https://doi.org/10.11648/j.ajp.20200601.11

Jennifer, S. S., Shamim, M. H., Reza, A. W., et al. (2023). Sickle cell disease classification using deep learning. Heliyon, 9(11), e22203. https://doi.org/10.1016/j.heliyon.2023.e22203

Kandel, I., Castelli, M., & Manzoni, L. (2022). Brightness as an Augmentation Technique for Image Classification. Emerging Science Journal, 6(4), 881–892. https://doi.org/10.28991/esj-2022-06-04-015

Khachnaoui, H., Agrebi, M., Halouani, S., et al. (2022). Deep Learning for Automatic Pulmonary Embolism Identification Using CTA Images. In: Proceedings of the 2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). https://doi.org/10.1109/atsip55956.2022.9805929

Li, D., Yi, J., Han, G., et al. (2022). MALDI-TOF Mass Spectrometry in Clinical Analysis and Research. ACS Measurement Science Au, 2(5), 385–404. https://doi.org/10.1021/acsmeasuresciau.2c00019

Loey, M., Naman, M. R., & Zayed, H. H. (2020). A Survey on Blood Image Diseases Detection Using Deep Learning. International Journal of Service Science, Management, Engineering, and Technology, 11(3), 18–32. https://doi.org/10.4018/ijssmet.2020070102

Nanni, L., Paci, M., Brahnam, S., et al. (2021). Comparison of Different Image Data Augmentation Approaches. Journal of Imaging, 7(12), 254. https://doi.org/10.3390/jimaging7120254

Nguyen-Khoa, T., Mine, L., Allaf, B., et al. (2018). Sickle SCAN™ (BioMedomics) fulfills analytical conditions for neonatal screening of sickle cell disease. Annales de Biologie Clinique, 76(4), 416–420. https://doi.org/10.1684/abc.2018.1354

Reza, M. T., Mehedi, N., Tasneem, N. A., et al. (2019). Identification of Crop Consuming Insect Pest from Visual Imagery Using Transfer Learning and Data Augmentation on Deep Neural Network. In: Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT). https://doi.org/10.1109/iccit48885.2019.9038450

Righetti, P. G. (1983). Isoelectric focusing: Theory, methodology, and applications. Elsevier Science.

Salman Khan, M., Ullah, A., Khan, K. N., et al. (2022). Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images. Diagnostics, 12(10), 2405. https://doi.org/10.3390/diagnostics12102405

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0

Tian, Y., Cao, Y., Zha, G., et al. (2023). Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning. Analytical Chemistry, 95(37), 13941–13948. https://doi.org/10.1021/acs.analchem.3c02461

United Nations. (2009). Resolution adopted by the General Assembly on 22 December 2008. Available online: https://digitallibrary.un.org/record/644334/files/A_RES_63_237-EN.pdf?version=1 (accessed on 21 January 2024).

World Health Organization. (2006). Fifty-ninth World Health Assembly, Geneva, 22–27 May 2006: Reports of Committees. Available online: https://iris.who.int/bitstream/handle/10665/21483/WHA59_REC3-en.pdf?sequence=1&isAllowed=y (accessed on 21 January 2024).

Xu, M., Papageorgiou, D. P., Abidi, S. Z., et al. (2017). A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLOS Computational Biology, 13(10), e1005746. https://doi.org/10.1371/journal.pcbi.1005746

Yan, R., Ren, F., Wang, Z., et al. (2020). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173, 52–60. https://doi.org/10.1016/j.ymeth.2019.06.014




DOI: https://doi.org/10.24294/jipd.v8i9.6121

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